Well logging tool and interpretation framework that employs a system of artificial neural networks for quantifying mud and formation electromagnetic properties

Methods and systems are provided that predict electromagnetic properties of drilling mud and a formation, which involve a logging tool that measures current injected into a measurement zone adjacent a sensor electrode at multiple frequencies. The measured currents at the multiple frequencies are processed to determine complex impedances for the sensor electrode at the multiple frequencies. The complex impedances are used to generate input data, which is supplied to a system of artificial neural networks (ANNs) that is configured to predict and output electromagnetic properties of the drilling mud and the formation within the measurement zone and possibly tool standoff based on the input data. The system of ANNs can employ a cascaded architecture of multiple ANNs. The electromagnetic properties or tool standoff predicted by the system of ANNs can be used to construct a borehole image over varying azimuth and depth.